World's Best Scientists 2026 revealed!
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Computer Science
China
2026

D-Index & Metrics

Computer Science

D-Index
101
Citations
36629
World Ranking
354
National Ranking
42

Research.com Recognitions

  • 2026 - Research.com Computer Science in China Leader Award
  • 2025 - Research.com Computer Science in China Leader Award
  • 2022 - Research.com Computer Science in China Leader Award

Overview

Liang Gao is affiliated with Huazhong University of Science and Technology in China and works primarily in the field of Engineering. Their research spans several subfields including Industrial and Manufacturing Engineering, Civil and Structural Engineering, Artificial Intelligence, Control and Systems Engineering, and Electrical and Electronic Engineering.

The main research topics Liang Gao has focused on include:

  • Scheduling and Optimization Algorithms
  • Advanced Manufacturing and Logistics Optimization
  • Topology Optimization in Engineering
  • Advanced Multi-Objective Optimization Algorithms
  • Assembly Line Balancing Optimization
  • Advanced Battery Technologies Research
  • Manufacturing Process and Optimization

Liang Gao has authored or coauthored research papers published in frequent venues such as:

  • Swarm and Evolutionary Computation
  • Computer Methods in Applied Mechanics and Engineering
  • Journal of Manufacturing Systems
  • SSRN Electronic Journal
  • Robotics and Computer-Integrated Manufacturing

Recent notable papers by Liang Gao include:

  • "An Effective Cooperative Co-Evolutionary Algorithm for Distributed Flowshop Group Scheduling Problems" (2020), published in IEEE Transactions on Cybernetics
  • "A Review on Recent Advances in Vision-based Defect Recognition towards Industrial Intelligence" (2021), published in Journal of Manufacturing Systems
  • "Energy-Efficient Scheduling of Distributed Flow Shop With Heterogeneous Factories: A Real-World Case From Automobile Industry in China" (2020), published in IEEE Transactions on Industrial Informatics
  • "A Surrogate-Assisted Multiswarm Optimization Algorithm for High-Dimensional Computationally Expensive Problems" (2020), published in IEEE Transactions on Cybernetics
  • "Robustly printable freeform thermal metamaterials" (2021), published in Nature Communications

The scientist has collaborated extensively with coauthors including:

  • Xinyu Li
  • Mi Xiao
  • Akhil Garg
  • Weiming Shen
  • Wei Li

Liang Gao has also contributed to several book publications released by Springer Nature, such as:

  • "Isogeometric Topology Optimization" (2022)
  • "Effective Methods for Integrated Process Planning and Scheduling" (2020)
  • "Welding and Cutting Case Studies with Supervised Machine Learning" (2020)
  • "Intelligence Optimization for Green Scheduling in Manufacturing Systems" (2023)

Best Publications

  • A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method

    Long Wen;Xinyu Li;Liang Gao;Yuyan Zhang

  • A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis

    Long Wen;Liang Gao;Xinyu Li

  • A transfer convolutional neural network for fault diagnosis based on ResNet-50

    Long Wen;Xinyu Li;Liang Gao

  • An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem

    Guohui Zhang;Xinyu Shao;Peigen Li;Liang Gao

  • An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem

    Xinyu Li;Liang Gao

  • An effective genetic algorithm for the flexible job-shop scheduling problem

    Guohui Zhang;Liang Gao;Yang Shi

  • Energy-efficient permutation flow shop scheduling problem using a hybrid multi-objective backtracking search algorithm

    Chao Lu;Liang Gao;Xinyu Li;Quanke Pan

  • An improved fruit fly optimization algorithm for continuous function optimization problems

    Quan-Ke Pan;Quan-Ke Pan;Hong-Yan Sang;Jun-Hua Duan;Liang Gao

  • Integration of process planning and scheduling-A modified genetic algorithm-based approach

    Xinyu Shao;Xinyu Li;Liang Gao;Chaoyong Zhang

  • Parameter extraction of photovoltaic models using an improved teaching-learning-based optimization

    Shuijia Li;Wenyin Gong;Xuesong Yan;Chengyu Hu

  • Cellular particle swarm optimization

    Yang Shi;Hongcheng Liu;Liang Gao;Guohui Zhang

  • Effective heuristics and metaheuristics to minimize total flowtime for the distributed permutation flowshop problem

    Quan-Ke Pan;Quan-Ke Pan;Liang Gao;Ling Wang;Jing Liang

  • A multi-objective genetic algorithm based on immune and entropy principle for flexible job-shop scheduling problem

    Xiaojuan Wang;Liang Gao;Chaoyong Zhang;Xinyu Shao

  • Queuing search algorithm: A novel metaheuristic algorithm for solving engineering optimization problems

    Jinhao Zhang;Mi Xiao;Liang Gao;Quanke Pan

  • A Review on Recent Advances in Vision-based Defect Recognition towards Industrial Intelligence

    Yiping Gao;Xinyu Li;Xi Vincent Wang;Lihui Wang

  • A novel mathematical model and multi-objective method for the low-carbon flexible job shop scheduling problem

    Lvjiang Yin;Xinyu Li;Liang Gao;Chao Lu

  • An adaptive process planning approach of rapid prototyping and manufacturing

    G.Q. Jin;W.D. Li;L. Gao

  • Review on flexible job shop scheduling

    Jin Xie;Liang Gao;Kunkun Peng;Xinyu Li

  • Imbalanced data fault diagnosis of rotating machinery using synthetic oversampling and feature learning

    Yuyan Zhang;Xinyu Li;Liang Gao;Lihui Wang

  • A hybrid multi-objective grey wolf optimizer for dynamic scheduling in a real-world welding industry

    Chao Lu;Liang Gao;Xinyu Li;Shengqiang Xiao

  • An Effective Cooperative Co-Evolutionary Algorithm for Distributed Flowshop Group Scheduling Problems.

    Quan-Ke Pan;Liang Gao;Ling Wang

  • A differential evolution algorithm with self-adapting strategy and control parameters

    Quan-Ke Pan;P. N. Suganthan;Ling Wang;Liang Gao

  • A semi-supervised convolutional neural network-based method for steel surface defect recognition

    Yiping Gao;Liang Gao;Xinyu Li;Xuguo Yan

  • Mathematical modeling and evolutionary algorithm-based approach for integrated process planning and scheduling

    Xinyu Li;Liang Gao;Xinyu Shao;Chaoyong Zhang

Frequent Co-Authors

Xinyu Li
Xinyu Li Huazhong University of Science and Technology
Akhil Garg
Akhil Garg Huazhong University of Science and Technology
Xinyu Shao
Xinyu Shao Huazhong University of Science and Technology
Peigen Li
Peigen Li Huazhong University of Science and Technology
Weidong Li
Weidong Li Wuhan University of Technology
Zhen Luo
Zhen Luo University of Technology Sydney
Chaoyong Zhang
Chaoyong Zhang Huazhong University of Science and Technology
Ling Wang
Ling Wang Tsinghua University
Weiming Shen
Weiming Shen Huazhong University of Science and Technology
Lihui Wang
Lihui Wang Royal Institute of Technology

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